Artificial Intelligence in Healthcare: Transforming the Future of Medicine
Article Sidebar
Main Article Content
Abstract: Healthcare is revolutionized by the unimaginable speed with which AI has evolved. The AI technologies, in particular, are at the forefront in diagnostics, personalization, and the simplification of administrative and R&D processes in the pharmaceutical industry. The investigation discusses the health care applications and advantages along with the problems and ethical issues that are traits of AI systems. Between major case studies and new strides in AI, technology is making health outcomes better while it is protecting patients' privacy, stopping biases, and reducing the need for human intervention.
Downloads
References
Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S.& Wang,Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4), 230-243.
Mesko, B., Hepp, B., Fiath, R., & Topol, E. J. (2018). Digital health. Nature Biotechnology, 36(7), 591-597.
World Health Organization. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.
Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Biotechnology, 37(1), 59-64.
Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342–1350. https://doi.org/10.1038/s41591-018-0107-6
Rajkomar, A., Oren, E., Chen, K., et al. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1, Article 18. https://doi.org/10.1038/s41746-018-0029-1
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinas inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25, 44–56. https://doi.org/10.1038/s41591-018-0300-7
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.